1. Statistics & Evidence-Based Practice
The Pennsylvania State university
College of nursing
Nursing 200w

2. Objectives Identify the purposes of statistical analyses.
Describe the process of data analysis.
Describe probability theory and decision theory that guide statistical data analysis.
Describe the process of inferring from a sample to a population.
Discuss the distribution of the normal curve.

3. Objectives Identify descriptive analyses.
Describe the results obtained from inferential statistical analyses.
Describe the five types of results obtained from quasi- experimental and experimental study designs.
Compare and contrast statistical significance and clinical importance of results.
Critically appraise statistical results, findings, limitations, conclusions, and generalization of findings.

4. A Statistical Primer

5. Statistics in Nursing Practice
Reading or critiquing published research
Examining outcomes of nursing practice by analyzing data collected in a clinical site
Developing administrative reports with support data
Analyzing research done by nursing staff and other health professionals at a clinical site
Demonstrating a problem or need and conducting a study

6. Critically Appraising Statistics
Identify statistical procedures used
Determine whether statistics used were appropriate or not
Evaluate researchers interpretation of statistics

7. Stages in Data Analysis
Prepare data for analysis.
Describe the sample.
Test reliability of measurement methods.
Conduct exploratory analysis.
Conduct confirmatory analysis guided by hypotheses, questions, or objectives.
Conduct posthoc analyses.

8. Major Statistics in Nursing Studies
Descriptive
Inferential

9. Descriptive Statistics
Describe and summarize the sample and variables
Also referred to as summary statistics

10. Inferential Statistics
Infer or address the objectives, questions, and hypotheses

11. Descriptive Statistics
If a research study collects numerical data, data analysis begins with descriptive statistics
Not limited to quantitative research!
May be the only statistical analysis conducted in a descriptive study

12. Types of Descriptive Statistics
Frequency distributions
Measures of central tendency
Measures of dispersion

13. Two-Tailedness

14. Ungrouped Frequency Distribution
Data in raw form:
1: ☺
2: ☺ ☺ ☺ ☺ ☺ ☺ ☺
3: ☺ ☺
4: ☺ ☺ ☺ ☺
5: ☺

15. Grouped Frequency Distribution
Data are grouped into categories:
Ages 15 to 20: 12
Ages 20 to 25: 14
Ages 25 to 30: 19….

16. Example of a Percentage Distribution
Housing: 41.7%
Textbooks: 8.3%
Clothing: 16.7%
Food: 8.3%
Additional Supplies: 25%

17. How Frequency Distributions are Presented in Research Articles

18. Measures of Central Tendency
Mean
Median
Mode

19. Normal Curve

20. Normal Curve
A theoretical frequency distribution of all possible values in a population
Levels of significance and probability are based on the logic of the normal curve

21. Mean
Is the sum of values divided by the number of values being summed

22. Median
Is the value in exact center of ungrouped frequency distribution
Is obtained by rank ordering the values

23. Mode
Is the numerical value or score that occurs with greatest frequency
Is expressed graphically

24. Bimodal Distribution

25. Measures of Dispersion
Range
Variance
Standard deviation
Standardized scores
Scatterplots

26. Range
Is obtained by subtracting lowest score from highest score

27. Difference Scores Are obtained by subtracting the mean from each score
Sometimes referred to as a deviation score because it indicates the extent to which a score deviates from the mean

28. Standard Deviation Is the square root of the variance
Just as the mean is the “average” value, the standard deviation is the “average” difference score

29. Standardized Scores
Raw scores that cannot be compared and are transformed into standardized scores
Common standardized score is a Z-score
Provides a way to compare scores in a similar process

30. Scatterplots

31. Probability Theory

32. Probability Theory Used to explain: Extent of a relationship
Probability of an event occurring
Probability that an event can be accurately predicted

33. Probability If probability is 0.23, then p = 0.23
There is a 23% probability that a particular event will occur

34. Inferences A conclusion or judgment based on evidence
Judgments are made based on statistical results

35. Decision Theory

36. Decision Theory
Assumes that all the groups in a study used to test a hypothesis are components of the same population relative to the variables under study
It is up to the researcher to provide evidence that there really is a difference
To test the assumption of no difference, a cutoff point is selected before analysis

37. Statistics
Judging the appropriateness of the statistical tests used

38. Critical Appraisal Factors that must be considered include:
Study purpose
Hypotheses, questions, or objectives
Design
Level of measurement

39. Critical Appraisal
You must judge whether the procedure was performed appropriately and the results were interpreted correctly.

40. Information Needed
Decide whether the research question focuses on differences or associations/relationships.

42. Information Needed
Decide whether the research question focuses on differences or associations/relationships.
Determine level of measurement.

44. Data Types
Nominal
Ordinal
Interval/Ratio

45. Information Needed
Decide whether the research question focuses on differences or associations/relationships.
Determine level of measurement.
Select the study design that most closely fits the one you are looking at.

47. Information Needed
Decide whether the research question focuses on differences or associations/relationships.
Determine level of measurement.
Select the study design that most closely fits the one you are looking at.
Determine whether the study samples are independent, dependent, or mixed.

49. Statistical Tests
Some common statistical tests in research

50. Chi-Square Nominal or ordinal data
Tests for differences between expected frequencies if groups are alike and frequencies actually observed in the data

51. Chi-Square
Regular No Regular Exercise Exercise Total Male Female Total

52. Chi-Square
Indicate that there is a significant difference between some of the cells in the table
The difference may be between only two of the cells, or there may be differences among all of the cells.
Chi-square results will not tell you which cells are different.

53. Example

54. Pearson Product-Moment Correlation
Tests for the presence of a relationship between two variables
Works with all types of data

55. Correlation Performed on data collected from a single sample
Measures of the two variables to be examined must be available for each subject in the data set.

56. Correlation Results Nature of the relationship (positive or negative)
Magnitude of the relationship (–1 to +1)
Testing the significance of a correlation coefficient

57. Response Question Which are the following are significant?
A. r = 0.56 (p = 0.03)
B. r = –0.13 (p = 0.2)
C. r = 0.65 (p < 0.002)

58. Example

59. Factor Analysis
Examines relationships among large numbers of variables
Disentangles those relationships to identify clusters of variables most closely linked
Sorts variables according to how closely related they are to the other variables
Closely related variables grouped into a factor

60. Factor Analysis Several factors may be identified within a data set
The researcher must explain why the analysis grouped the variables in a specific way
Statistical results indicate the amount of variance in the data set that can be explained by each factor and the amount of variance in each factor that can be explained by a particular variable

61. Regression Analysis
Used when one wishes to predict the value of one variable based on the value of one or more other variables

62. Regression Analysis
The outcome of analysis is the regression coefficient R
When R is squared, it indicates the amount of variance in the data that is explained by the equation
R2 = 0.63

63. Example

64. T-test Requires interval level measures
Tests for significant differences between two samples
Most commonly used test of differences

65. Example

66. Analysis of Variance ANOVA Tests for differences between means
Allows for comparison of groups

67. Example

68. Results
A summary of the types of results you will find in experimental and quasi-experimental research studies

69. Types of Results Significant and predicted Nonsignificant
Significant and not predicted
Mixed
Unexpected

70. Significant and Predicted
Support logical associations between variables
As expected by the researcher

71. Nonsignificant Negative or inconclusive results
No significant differences or relationships

72. Significant and Unpredicted
Opposite of what was expected
Indicate potential flawed logic of researcher

73. Mixed Most common outcome of studies
One variable may uphold predicted characteristics, whereas another does not
Or two dependent measures of the same variable may show opposite results.

74. Unexpected
Relationships between variables that were not hypothesized and not predicted from the framework being used

75. Findings, Conclusions, & Implications

76. Findings
Results of a research study that have been translated and interpreted

77. Statistically Significant Findings
Significant p-values

78. Clinically Significant Findings
Practical application of findings
Somewhat based on opinion

79. Conclusions A synthesis of findings
Researchers should not go beyond what the findings state or interpret too much!

80. Implications
The meaning for nursing practice, research, and/or education
Specific suggestions for implementing the findings

81. Critical Appraisal
Questions to ask

82. Critical Appraisal
What statistics were used to described the characteristics of the sample?
Are the data analysis procedures clearly described?
Did statistics address the purpose of the study?
Did the statistics address the objectives, questions or hypotheses of the study?
Were the statistics appropriate for the level of measurement of each variable?

83. Critical Appraisal
What statistics were used to described the characteristics of the sample?
Are the data analysis procedures clearly described?
Did statistics address the purpose of the study?
Did the statistics address the objectives, questions or hypotheses of the study?
Were the statistics appropriate for the level of measurement of each variable?

84. Critical Appraisal
What statistics were used to described the characteristics of the sample?
Are the data analysis procedures clearly described?
Did statistics address the purpose of the study?
Did the statistics address the objectives, questions or hypotheses of the study?
Were the statistics appropriate for the level of measurement of each variable?

85. Critical Appraisal
What statistics were used to described the characteristics of the sample?
Are the data analysis procedures clearly described?
Did statistics address the purpose of the study?
Did the statistics address the objectives, questions or hypotheses of the study?
Were the statistics appropriate for the level of measurement of each variable?

86. Critical Appraisal
What statistics were used to described the characteristics of the sample?
Are the data analysis procedures clearly described?
Did statistics address the purpose of the study?
Did the statistics address the objectives, questions or hypotheses of the study?
Were the statistics appropriate for the level of measurement of each variable?

87. Critical Appraisal
What statistics were used to described the characteristics of the sample?
Are the data analysis procedures clearly described?
Did statistics address the purpose of the study?
Did the statistics address the objectives, questions or hypotheses of the study?
Were the statistics appropriate for the level of measurement of each variable?

88. The End!
Question?
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